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echosonic

At echosonic, we are revolutionizing microphones by bringing time-series signal processing inside MEMS sensors by using the mechanical element as the computational source. This enables Machine Learning capabilities into the microphone to reduce our dependency on the cloud and other centralized infrastructures for audio recognition. Oh, just a tiny little breakthrough, no big deal. 

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Original audio never leaves the device

Reduce >85% of the computational load related to audio processing

On-demand and secure data transfer when necessary

Benefits in IoT

Applications

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              Audio-based predictive maintenance

Signature

Detection

+    Minimal data required

+    Working in noisy environment

+    Untraceable computational demand and power consumption

+    Easy and low-coast implementation

  • Detect abnormal sound produce by machine

  • Send real-time warning and/or stop the production

  • Allow detection and early identification of problems

  • Recoverable original audio data for later analysis

Our Story

We are a group of young and energetic physicists, computer scientists, mechanical engineers, and data scientists aiming to disrupt the edge AI market. With robust and continuous university collaborations and public-private cooperation, we are bringing the cutting-edge research of deep technology to human-centric machine learning and building a public forum for disseminating computational technologies.

Our Technology

Backed by the university research, echosonic exploits the dynamic mechanisms of microphones to filter out the important part of audio signals for learning-based processing. The hardware/software audio processing pipeline can be deployed to edge sensors, eliminating privacy and security concern, and draws only 10% of power consumption compared to the legacy devices. 

Low power consumption

Exceptional processing speed

Benefits

Reduce >90% power consumption of intelligent audio processing compared to legacy devices

Privacy and security

Eliminates privacy and security concerns by processing audio signals internally on microphones

Exceptional processing speed by reducing latency in cloud-microphone data transferring

Press kit

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